Apparatus and method for identifying condition of animal object based on image

ABSTRACT

An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

TECHNICAL FIELD

The present disclosure relates to an apparatus and method foridentifying the condition of an animal object based on an image.

BACKGROUND

With recent advances in image processing and various IT technologies, asystem capable of automatically monitoring the condition of an animal ina shed or the like is being actively developed. In particular, atechnique of automatically recognizing each animal object andautomatically classifying the behavior of the animal object by usingCCTV images is being developed.

However, it is difficult to accurately detect a plurality of dynamicallymoving animals by such an image processing technique alone.

According to a conventional technology known as a universal objectdetection technology, detection information about an object of interestis represented by an axis-aligned bounding box. Such a bounding box canusually be used to detect an animal object in most cases. However, ifthe object is d with an axis, a broad background area, which does notactually correspond to the object, can be overestimated as an objectarea. In particular, if a plurality of animals of the same class iscrowded and overlaps each other, a detection area for each animal may beoverestimated, which may result in a great decrease in detectionaccuracy for each of the crowded animals.

In a general method of measuring the conditions of livestock animalsraised in a shed, a farmer or a manager directly observes each of thelivestock animals and records observation data and determines the healthcondition of each livestock animal personally based on the observationdata or consults a livestock expert or a veterinarian.

In this regard, Korean Patent No. 10-2172347 (entitled “Method andsystem for determining health status of farm livestock”) discloses amethod for checking the health condition of each livestock animal bysetting a reference line based on an image.

However, according to the conventional technology, an image taken at anarbitrary reference time with a camera installed at a farm is analyzedto extract an outline of each livestock animal, each livestock animal issorted by using the extracted outline, and a reference line isdetermined for each sorted livestock animal. That is, the conventionaltechnology is a technology for evaluating the health condition of alivestock animal as one of normal and abnormal by using the determinedreference line. In other words, checking the health condition just bysetting a reference line has low accuracy, and the conventionaltechnology makes it possible to check only a simple health condition ofa livestock animal.

To solve this problem, the present disclosure proposes a method by whichtime series information about a position and pose of a livestock animalin an image is generated to train a deep learning model and the currentcondition of the livestock animal is identified using the trained deeplearning model.

SUMMARY

In view of the foregoing, the present disclosure is conceived to providean apparatus and method for identifying the condition of an animalobject based on an image by which a bounding box trained to be suitablefor the animal object is used to extract animal detection information,and, thus, animal condition information can be output.

However, the problems to be solved by the present disclosure are notlimited to the above-described problems. There may be other problems tobe solved by the present disclosure.

A first aspect of the present disclosure provides an image-based animalobject condition identification apparatus including: a communicationmodule that receives an image of an object; a memory that stores thereina program configured to extract animal condition information from thereceived image; and a processor that executes the program. The programextracts continuous animal detection information of each object byinputting the received image into an animal detection model that istrained based on learning data composed of animal images and determinespredetermined animal condition information for each class of each animalobject by inputting the continuous animal detection information of eachobject into an animal condition identification model. The animaldetection information is extracted from n number of continuous entireimages including at least one animal object, and includes n number ofcontinuous object images and n number of continuous object detectiondata corresponding to the respective object images.

A second aspect of the present disclosure provides a method foridentifying a condition of an animal object based on an image by usingan image-based animal object condition identification apparatus,including: receiving an image of an object; extracting continuous animaldetection information of each object by inputting the received imageinto an animal detection model that is trained based on learning datacomposed of animal images; and outputting predetermined animal conditioninformation for each class of each animal object by inputting thecontinuous animal detection information of each object into an animalcondition identification model. The animal detection information isextracted from n number of continuous entire images including at leastone animal object, and includes n number of continuous object images andn number of continuous object detection data corresponding to therespective object images.

According to an embodiment of the present disclosure, unlike theconventional object detection technology, a bounding box can be used toextract animal detection information from n number of continuous entireimages, and, thus, it is possible to greatly improve detection accuracyfor crowded livestock.

Also, according to an embodiment of the present disclosure, unlike atechnology of identifying the condition of a livestock animal using onlyone static datum, animal condition information is output using dynamicdata, and, thus, it is possible to more accurately identify abnormalconditions of livestock animals.

Further, according to an embodiment of the present disclosure, animalcondition information is accumulatively recorded, and, thus, a mangercan efficiently monitor records of abnormal condition of each animalobject. Furthermore, according to an embodiment of the presentdisclosure, it is possible to implement various applications formonitoring an animal object such as sending a notice to a manager incase of abnormal condition (being stuck, collapse, delivery, etc.) of alivestock animal.

BRIEF DESCRIPTION OF THE DRAWINGS

In the detailed description that follows, embodiments are described asillustrations only since various changes and modifications will becomeapparent to those skilled in the art from the following detaileddescription. The use of the same reference numbers in different figuresindicates similar or identical items.

FIG. 1 is a block diagram illustrating a configuration of an image-basedanimal object condition identification apparatus according to anembodiment of the present disclosure.

FIG. 2 is a block diagram provided to explain a process of outputtinganimal condition information by the image-based animal object conditionidentification apparatus according to an embodiment of the presentdisclosure.

FIG. 3A is provided to explain animal detection information extracted byan animal detection model of the image-based animal object conditionidentification apparatus according to an embodiment of the presentdisclosure.

FIG. 3B and FIG. 3C are provided to explain an animal conditionidentification model of the image-based animal object conditionidentification apparatus according to an embodiment of the presentdisclosure.

FIG. 4 and FIG. 5 are provided to explain animal detection informationextracted by the animal detection model of the image-based animal objectcondition identification apparatus according to an embodiment of thepresent disclosure.

FIG. 6 , FIG. 7 and FIG. 8 are provided to explain an animal detectionmodel of the image-based animal object condition identificationapparatus according to an embodiment of the present disclosure.

FIG. 9 is a flowchart illustrating a method for identifying thecondition of an animal object by using the image-based animal objectcondition identification apparatus according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Hereafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings. However, it is to benoted that the present disclosure is not limited to the embodiments butcan be embodied in various other ways. Also, the accompanying drawingsare provided to help easily understand the embodiments of the presentdisclosure and the technical conception described in the presentdisclosure is not limited by the accompanying drawings. In the drawings,parts irrelevant to the description are omitted for the simplicity ofexplanation, and the size, form and shape of each component illustratedin the drawings can be modified in various ways. Like reference numeralsdenote like parts through the whole document.

Suffixes “module” and “unit” used for components disclosed in thefollowing description are merely intended for easy description of thespecification, and the suffixes themselves do not give any specialmeaning or function. Further, in the following description of thepresent disclosure, a detailed explanation of known related technologiesmay be omitted to avoid unnecessarily obscuring the subject matter ofthe present disclosure.

Throughout this document, the term “connected to (contacted with orcoupled to)” may be used to designate a connection or coupling of oneelement to another element and includes both an element being “directlyconnected to (contacted with or coupled to)” another element and anelement being “electronically connected to (contacted with or coupledto)” another element via another element. Further, through the wholedocument, the term “comprises or includes” and/or “comprising orincluding” used in the document means that one or more other components,steps, operation and/or existence or addition of elements are notexcluded in addition to the described components, steps, operationand/or elements unless context dictates otherwise.

Further, in describing components of the present disclosure, ordinalnumbers such as first, second, etc. can be used only to differentiatethe components from each other, but do not limit the sequence orrelationship of the components. For example, a first component of thepresent disclosure may also be referred to as a second component andvice versa.

FIG. 1 is a block diagram illustrating a configuration of an image-basedanimal object condition identification apparatus according to anembodiment of the present disclosure.

Referring to FIG. 1 , an image-based animal object conditionidentification apparatus 100 includes a communication module 110, amemory 120 and a processor 130 and may further include a database 140.The image-based animal object condition identification apparatus 100receives images from a plurality of CCTVs installed at a shed in realtime, detects an animal object by using the received images and detectsthe condition of the animal based on the animal detection information.

To this end, the image-based animal object condition identificationapparatus 100 may be implemented with a computer or portable devicewhich can access a server or another device through a network. Herein,the computer may include, for example, a notebook, a desktop and alaptop equipped with a WEB browser. The portable devices may be, forexample, a wireless communication device that ensures portability andmobility and may include all kinds of handheld-based wirelesscommunication devices such as various smart phones, tablet PCs, smartwatches, and the like.

The term “network” refers to a connection structure that enablesinformation exchange between nodes such as devices, servers, etc. andincludes LAN (Local Area Network), WAN (Wide Area Network), Internet(WWW: World Wide Web), a wired or wireless data communication network, atelecommunication network, a wired or wireless television network, andthe like. Examples of the wireless data communication network mayinclude 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (LongTerm Evolution), WIMAX (World Interoperability for Microwave Access),Wi-Fi, Bluetooth communication, infrared communication, ultrasoniccommunication, VLC (Visible Light Communication), LiFi, and the like,but may not be limited thereto.

The communication module 110 receives images of an object from one ormore cameras. Herein, the object may include various classes of animalobjects such as cows, pigs and dogs. The communication module 110 mayinclude hardware and software required to transmit and receive a signal,such as a control signal or a data signal, through wired/wirelessconnection with other network devices.

The memory 120 stores therein a program configured to extract animaldetection information from the images received through the communicationmodule 110. Herein, the program configured to extract animal detectioninformation extracts continuous animal detection information of eachobject by inputting the received images into an animal detection modelthat is trained based on learning data composed of animal images. Also,the program extracts animal condition information by inputting thecontinuous animal detection information of each object into an animalcondition identification model constructed based on learning data inwhich animal condition information is matched with each class of eachanimal object. Details of the animal detection information and animalcondition information will be described later.

Herein, the memory 120 may collectively refer to a non-volatile storagedevice that retains information stored therein even when power is notsupplied and a volatile storage device that requires power to retaininformation stored therein. The memory 120 may function to temporarilyor permanently store data processed by the processor 130. The memory 120may include magnetic storage media or flash storage media in addition tothe volatile storage device that requires power to retain informationstored therein, but the present disclosure is not limited thereto.

The processor 130 executes the program configured to extract the animalcondition information stored in the memory 120 and outputs the animalcondition information about the object as a result of execution.

In an example, the processor 130 may be implemented as a microprocessor,a central processing unit (CPU), a processor core, a multiprocessor, anapplication-specific integrated circuit (ASIC) or a field programmablegate array (FPGA), but the scope of the present disclosure is notlimited thereto.

The database 140 may store therein images taken with the cameras andreceived through the communication module 110 or various data fortraining of the animal condition identification model. In particular,images taken with the respective cameras installed at each shed may bedistinguished and separately stored in the database 140. Also, thedatabase 140 accumulatively stores the animal detection information andanimal condition information extracted by the animal conditioninformation extraction program, and the animal detection information andanimal condition information can be used in various applications formonitoring an abnormal condition of an animal.

FIG. 2 is a block diagram provided to explain a process of outputtinganimal condition information by the image-based animal object conditionidentification apparatus according to an embodiment of the presentdisclosure.

Referring to FIG. 2 , the image-based animal object conditionidentification apparatus 100 extracts continuous animal detectioninformation 210 of each object by inputting received images 10 into ananimal detection model 600 that is trained based on learning datacomposed of animal images. Then, the image-based animal object conditionidentification apparatus 100 determines predetermined animal conditioninformation 310 for each class of each animal object by inputting thecontinuous animal detection information 210 of each object into ananimal condition identification model 30. Herein, the animal detectioninformation 210 is extracted from n number of continuous entire imagesincluding at least one animal object, and includes n number ofcontinuous object images 211 and n number of continuous object detectiondata corresponding to the respective object images 211.

FIG. 3A is provided to explain animal detection information extracted byan animal detection model of the image-based animal object conditionidentification apparatus according to an embodiment of the presentdisclosure.

FIG. 3B and FIG. 3C are provided to explain an animal conditionidentification model of the image-based animal object conditionidentification apparatus according to an embodiment of the presentdisclosure.

FIG. 4 and FIG. 5 are provided to explain animal detection informationextracted by the animal detection model of the image-based animal objectcondition identification apparatus according to an embodiment of thepresent disclosure.

Hereafter, the animal detection model 600 that generates the animaldetection information 210 will be described with reference to FIG. 3A,FIG. 4 and FIG. 5 .

Referring to FIG. 3A, the animal detection information 210 is extractedfrom n number of continuous entire images 200 including at least oneanimal object. That is, the animal detection information 210 includesthe n number of continuous object images 211 and n number of continuousobject detection data 212 corresponding to the respective object images211.

As shown in FIG. 4 and FIG. 5 , the object detection data 212 refer toinformation about a bounding box (rbbox) created to be suitable for ananimal object detected from the n number of continuous entire images200. That is, the object detection data 212 include coordinates (xc, yc)of a central point of the bounding box, a width (w) of the bounding box,a length (h) of the bounding box and a rotational angle (theta) of thebounding box with respect to a reference axis.

Also, the object detection data 212 refer to information indicatingkeypoints of the animal object. That is, the object detection data 212include a position (x1, y1) of the end of the head of the animal object,a position (x2, y2) of the neck and a position (xn, yn) of the end ofthe body.

The object detection data 212 may further include information about theclass of the animal object detected from the images and informationabout a pose of the animal object. The information about the class ofthe animal object may distinguish different species of animals such ascows, pigs and dogs, but is not limited thereto. For example, theinformation about the class of the animal object may distinguishdifferent growth stages of the same species. Pigs can be classified intosuckling pigs, weaning pigs, growing pigs, fed pigs, candidate pigs,pregnant pigs and farrowing pigs. Also, the information about a pose ofthe animal object may distinguish various poses such as sitting,standing, mounting behavior, rollover, and dog sitting.

As shown in FIG. 3A, the object images 211 may be composed of imagescropped to sizes of respective bounding boxes created to be suitable forthe animal object detected from the n number of continuous entire images200.

Examples of the object detection data 212 can be seen more clearly fromFIG. 4 . As described above, the bounding box of the present disclosureis created in consideration of the degree of rotation of the axis of theanimal object as a detection target (i.e., an object), and, thus, thebounding box can be optimized for the size of the animal object.

Referring to FIG. 3A again, the animal detection model 600 of thepresent disclosure is constructed based on the n number of continuousentire images 200 including at least one animal object and learning datain which animal condition information is matched with each class of eachanimal object included in each of the continuous entire images 200. Theanimal detection model 600 is trained through a training process andthen automatically extracts the animal detection information 210including n number of continuous object images 211 from the n number ofcontinuous entire images 200 and n number of continuous object detectiondata 212 corresponding to the respective object images 211 through aninference process on actually input images. A detailed configuration ofthe animal detection model 600 will be described later with reference toFIG. 6 through FIG. 8 .

Hereafter, an animal condition identification model 300 that generatesthe animal condition information 310 will be described with reference toFIG. 3B and FIG. 3C.

Referring to FIG. 3A and FIG. 3B, the animal condition identificationmodel 300 is constructed based on the n number of continuous entireimages 200 including at least one animal object and learning data inwhich the animal condition information 310 is matched with each class ofeach animal object included in each of the continuous entire images 200.

By way of example, the animal condition identification model 300includes a first feature extraction unit 301, a second featureextraction unit 302 and an output unit 303.

The first feature extraction unit 301 generates n number ofone-dimensional image data by converting the n number of continuousobject images 211 into monochrome images and generates feature data of afirst length based on the one-dimensional image data by using aconvolutional neural network (CNN). For example, the first featureextraction unit 301 can be further improved in performance by usingResNet or DenseNet, which is a CNN classifier model improved over theCNN.

The second feature extraction unit 302 generates n number ofone-dimensional data of a second length by connecting the n number ofcontinuous object detection data 212 and generates feature data of thesecond length based on the one-dimensional image data of the secondlength by using a first feed-forward neural network (FFNN).

The output unit 303 generates data of a third length by connecting thefeature data of the first length and the feature data of the secondlength and outputs the animal condition information 310 based on thedata of the third length by using a second FFNN.

Also, the output unit 303 is constructed to sort an abnormal conditionfrom the animal condition information 310 for each class of each animalobject by using the softmax function.

Referring to FIG. 3C, the animal condition information 310 is sorted bycodes indicating abnormal conditions of animals including stop, walk,run, limp, delivery, excretion, heat, fell over, stuck, biting and noserubbing.

For example, the second FFNN in the output unit 303 may finallygenerate, as an output value, the probability of each of the predefinedanimal condition information 310 as shown in FIG. 3C by using thesoftmax.

That is, an abnormal condition of a livestock animal is identified notwith only one static datum as in the conventional technology, but withdynamic data, and, thus, it is possible to improve the identificationaccuracy. Also, various abnormal conditions of livestock animals rangingfrom short-term animal condition information such as walk, run and limpto long-term animal condition information such as delivery and diseasecan be identified depending on the number of continuous entire images.

Hereafter, the animal detection model 600 that generates the animaldetection information 210 will be described.

FIG. 6 , FIG. 7 and FIG. 8 are provided to explain an animal detectionmodel of the image-based animal object condition identificationapparatus according to an embodiment of the present disclosure.

The animal detection model 600 includes a backbone 610, a neck 620 and ahead 630.

The backbone 610 is a component configured to extract a feature from theinput image and commonly used for deep neural network-based imageanalysis and processing. The backbone 610 is mainly configured as astack of 2D convolution layers as illustrated in FIG. 6 , and has beenimproved to have various neural network structures in order to improvethe efficiency thereof. Backbones of various structures commonlyfunction to receive an image and extract intermediate information. Theintermediate information is delivered to the neck 620.

The neck 620 collects the intermediate information from each layer ofthe backbone 610 based on the feature extracted by the backbone 610. Theneck 620 is a lower neural network forming a universal object detectorand functions to collect the intermediate information from each layer ofthe backbone 610 and analyze the intermediate information. The imageanalyzed in each layer has different resolutions. Thus, if an object isa long or short distance away, the neck 620 extracts intermediateinformation from each layer to effectively detect animals of varioussizes and provides the intermediate information to the head 630. Theneck 620 may have various configurations depending on the form of thebackbone 610. Specifically, the number of layers of a neural networkforming the neck 620 and a hyperparameter for each layer may varydepending on the form of the backbone 610.

The head 630 outputs object detection information based on theintermediate information collected by the neck 620. The head 630receives the intermediate information acquired by the neck 620 andoutputs animal detection information. The head 630 receives theintermediate information from each layer of the neck 620 and outputs theanimal detection information recognized by each layer. In particular,the head 630 of the present disclosure includes a plurality of animaldetection subnets 631, and each animal detection subnet 631 includes asubnet for extracting a bounding box and a keypoint, a subnet forextracting a class of an animal and a subnet for extracting a pose of ananimal as shown in FIG. 7 . That is, the animal detection model 600 mayextract the n number of continuous object images 211 and the n number ofcontinuous object detection data 212 corresponding to the respectiveobject images 211 by means of each animal detection subnet 631.

Meanwhile, a non-maximum suppression (NMS) module may be further coupledto an output end of the head 630. The NMS refers to an algorithm forselecting a bounding box with the highest similarity when severalbounding boxes are created for the same object. Since it is aconventional technology, a detailed description thereof will be omitted.

The subnet for extracting a bounding box and a keypoint is composed ofcascaded multi-lane deep convolutional networks. The cascaded multi-lanedeep convolutional networks are constructed according to a causal orderfor extracting a bounding box and a keypoint for a given animal image.Each of the object detection data 212 is defined from each imageaccording to the following causal order.

That is, as shown in FIG. 8 , a central point (xc, yc) and major points((x1, y1), (x2, y2), (x3, y3)) are marked first. Then, a tangent linepassing through the central point and one or more of the major points isdrawn. Finally, an area (plane) with the tangent line passing throughits center is defined.

In the cascaded multi-lane deep convolutional networks constructed asdescribed above, information is delivered according to the causal orderand each information is output. That is, a first lane outputs thecentral point and the keypoint, a second lane outputs a direction(theta) of the tangent line, and a third lane outputs a width and aheight of the area including the tangent line and the central point.

The learning data used in the training process of the animal detectionmodel 600 include a plurality of images and the animal detectioninformation 210 matched with each image. Herein, the animal detectioninformation 210 is manually extracted from each image. That is, when anoperator sees each image, the operator may use an appropriate SW tool todirectly input the animal detection information 210, or the animaldetection information 210 may be automatically input by an alreadydeveloped animal detector and then corrected or supplemented by theoperator. For example, the operator displays a bounding box inconsideration of a rotational direction of an animal object with respectto a reference axis for each animal object included in an image andcreates coordinates of a central point of each bounding box, a width ofthe bounding box, a length of the bounding box and a rotational angle ofthe bounding box with respect to a reference axis. Also, the operatorextracts information about the class or pose of the animal object anduses the information as learning data.

Hereafter, description of the same components as those shown in FIG. 1through FIG. 8 will be omitted.

FIG. 9 is a flowchart illustrating a method for identifying thecondition of an animal object by using the image-based animal objectcondition identification apparatus according to an embodiment of thepresent disclosure.

Referring to FIG. 9 , the method for identifying the condition of ananimal object by using the image-based animal object conditionidentification apparatus includes: a process of receiving the image 10of an object (S110); a process of extracting the continuous animaldetection information 210 of each object by inputting the received imageinto the animal detection model 600 that is trained based on learningdata composed of animal images (S120); and a process of outputting thepredetermined animal condition information 310 for each class of eachanimal object by inputting the continuous animal detection information210 of each object into the animal condition identification model 300(S130). The animal detection information 210 is extracted from the nnumber of continuous entire images 200 including at least one animalobject, and includes the n number of continuous object images 211 andthe n number of continuous object detection data 212 corresponding tothe respective object images 211.

The object detection data 212 refer to information about a bounding boxcreated to be suitable for an animal object detected from each of the nnumber of continuous entire images 200. That is, the object detectiondata 212 include coordinates of a central point of the bounding box, awidth of the bounding box, a length of the bounding box and a rotationalangle of the bounding box with respect to a reference axis. Also, theobject detection data 212 refer to information indicating keypoints ofthe animal object. That is, the object detection data 212 include aposition of the end of the head of the animal object, a position of theneck and a position of the end of the body.

The object images 211 are composed of images cropped to sizes ofrespective bounding boxes created to be suitable for the animal objectdetected from each of the n number of continuous entire images 200.

The animal condition identification model 300 is constructed based onthe n number of continuous entire images 200 including at least oneanimal object and learning data in which the animal conditioninformation is matched with each class of each animal object included ineach of the continuous entire images 200.

The animal condition identification model 300 generates n number ofone-dimensional image data by converting the n number of continuousobject images 211 into monochrome images. The animal conditionidentification model 300 includes the first feature extraction unit 301that generates feature data of a first length based on theone-dimensional image data by using a convolutional neural network(CNN). The animal condition identification model 300 includes the secondfeature extraction unit 302 that generates n number of one-dimensionaldata of a second length by connecting the n number of continuous objectdetection data 212 and generates feature data of the second length basedon the one-dimensional image data of the second length by using a firstfeed-forward neural network (FFNN). The animal condition identificationmodel 300 includes the output unit 303 that generates data of a thirdlength by connecting the feature data of the first length and thefeature data of the second length and outputs the animal conditioninformation 310 based on the data of the third length by using a secondFFNN.

The animal object condition identification method described above can beembodied in a storage medium including instruction codes executable by acomputer such as a program module executed by the computer. Acomputer-readable medium can be any usable medium which can be accessedby the computer and includes all volatile/non-volatile andremovable/non-removable media. Further, the computer-readable medium mayinclude all computer storage media. The computer storage media includeall volatile/non-volatile and removable/non-removable media embodied bya certain method or technology for storing information such ascomputer-readable instruction code, a data structure, a program moduleor other data.

It would be understood by a person with ordinary skill in the art thatvarious changes and modifications may be made based on the abovedescription without changing technical conception and essential featuresof the present disclosure. Thus, it is clear that the above-describedembodiments are illustrative in all aspects and do not limit the presentdisclosure. The scope of the present disclosure is defined by thefollowing claims. It shall be understood that all modifications andembodiments conceived from the meaning and scope of the claims and theirequivalents are included in the scope of the present disclosure.

EXPLANATION OF REFERENCE NUMERALS

-   -   100: Image-based animal object condition identification        apparatus    -   110: Communication module    -   120: Memory    -   130: Processor    -   140: Database

We claim:
 1. An image-based animal object condition identificationapparatus, comprising: a communication module that receives an image ofan object; a memory that stores therein a program configured to extractanimal condition information from the received image; and a processorthat executes the program, wherein the program extracts continuousanimal detection information of each object by inputting the receivedimage into an animal detection model that is trained based on learningdata composed of animal images and outputs predetermined animalcondition information for each class of each animal object by inputtingthe continuous animal detection information of each object into ananimal condition identification model, and the animal detectioninformation is extracted from n number of continuous entire imagesincluding at least one animal object, and includes n number ofcontinuous object images and n number of continuous object detectiondata corresponding to the respective object images.
 2. The image-basedanimal object condition identification apparatus of claim 1, wherein theobject detection data include, as information about a bounding boxcreated to be suitable for an animal object detected from each of the nnumber of continuous entire images, coordinates of a central point ofthe bounding box, a width of the bounding box, a length of the boundingbox and a rotational angle of the bounding box with respect to areference axis and includes, as information indicating keypoints of theanimal object, a position of the end of the head of the animal object, aposition of the neck and a position of the end of the body, and theobject images are composed of images cropped to sizes of the respectivebounding boxes created to be suitable for the animal object detectedfrom each of the n number of continuous entire images.
 3. Theimage-based animal object condition identification apparatus of claim 1,wherein the animal condition identification model is constructed basedon the n number of continuous entire images including at least oneanimal object and learning data in which the animal conditioninformation is matched with each class of each animal object included ineach of the continuous entire images, and the animal conditionidentification model includes: a first feature extraction unit thatgenerates n number of one-dimensional image data by converting the nnumber of continuous object images into monochrome images and generatesfeature data of a first length based on the one-dimensional image databy using a convolutional neural network (CNN); a second featureextraction unit that generates n number of one-dimensional data of asecond length by connecting the n number of continuous object detectiondata and generates feature data of the second length based on theone-dimensional image data of the second length by using a firstfeed-forward neural network (FFNN); and an output unit that generatesdata of a third length by connecting the feature data of the firstlength and the feature data of the second length and outputs the animalcondition information based on the data of the third length by using asecond FFNN.
 4. The image-based animal object condition identificationapparatus of claim 3, wherein the output unit is constructed to sort anabnormal condition from the animal condition information for each classof each animal object by using the softmax function, and the animalcondition information is sorted by codes indicating abnormal conditionsof animals including stop, walk, run, limp, delivery, excretion, heat,fell over, stuck, biting and nose rubbing.
 5. The image-based animalobject condition identification apparatus of claim 1, wherein theprogram further extracts, as the object detection data, informationabout the class of the animal object detected from the image andinformation about a pose of the animal object.
 6. The image-based animalobject condition identification apparatus of claim 1, wherein the animaldetection model is constructed based on the n number of continuousentire images including at least one animal object and learning data inwhich the animal detection information is matched with the animal objectincluded in each of the continuous entire images, and the animaldetection model includes a backbone configured to extract a feature fromthe input image, a neck configured to collect intermediate informationfrom each layer of the backbone based on the feature extracted by thebackbone, and a head configured to output the animal detectioninformation based on the intermediate information collected by the neck.7. The image-based animal object condition identification apparatus ofclaim 6, wherein the head of the animal detection model extracts abounding box of the animal object and a keypoint of the animal objectbased on cascaded multi-lane deep convolutional networks, and thecascaded multi-lane deep convolutional networks are constructed toperform a process of extracting coordinates of a major keypoint, aprocess of extracting a direction of a tangent line passing through thecoordinates of the major keypoint and a process of extracting a widthand a height of an area including the tangent line and the majorkeypoint.
 8. The image-based animal object condition identificationapparatus of claim 6, wherein the head of the animal detection model isconstructed to extract each of information about the class of the animalobject and information about a pose of the animal object based on asingle-lane deep convolutional network.
 9. A method for identifying acondition of an animal object based on an image by using an image-basedanimal object condition identification apparatus, comprising: receivingan image of an object; extracting continuous animal detectioninformation of each object by inputting the received image into ananimal detection model that is trained based on learning data composedof animal images; and outputting predetermined animal conditioninformation for each class of each animal object by inputting thecontinuous animal detection information of each object into an animalcondition identification model, wherein the animal detection informationis extracted from n number of continuous entire images including atleast one animal object, and includes n number of continuous objectimages and n number of continuous object detection data corresponding tothe respective object images.
 10. The method for identifying a conditionof an animal object based on an image of claim 9, wherein the objectdetection data include, as information about a bounding box created tobe suitable for an animal object detected from each of the n number ofcontinuous entire images, coordinates of a central point of the boundingbox, a width of the bounding box, a length of the bounding box and arotational angle of the bounding box with respect to a reference axisand includes, as information indicating keypoints of the animal object,a position of the end of the head of the animal object, a position ofthe neck and a position of the end of the body, and the object imagesare composed of images cropped to sizes of the respective bounding boxescreated to be suitable for the animal object detected from each of the nnumber of continuous entire images.
 11. The method for identifying acondition of an animal object based on an image of claim 9, wherein theanimal condition identification model is constructed based on the nnumber of continuous entire images including at least one animal objectand learning data in which the animal condition information is matchedwith each class of each animal object included in each of the continuousentire images, and the animal condition identification model includes: afirst feature extraction unit that generates n number of one-dimensionalimage data by converting the n number of continuous object images intomonochrome images and generates feature data of a first length based onthe one-dimensional image data by using a convolutional neural network(CNN); a second feature extraction unit that generates n number ofone-dimensional data of a second length by connecting the n number ofcontinuous object detection data and generates feature data of thesecond length based on the one-dimensional image data of the secondlength by using a first feed-forward neural network (FFNN); and anoutput unit that generates data of a third length by connecting thefeature data of the first length and the feature data of the secondlength and outputs the animal condition information based on the data ofthe third length by using a second FFNN.
 12. The method for identifyinga condition of an animal object based on an image of claim 11, whereinthe output unit is constructed to sort an abnormal condition from theanimal condition information for each class of each animal object byusing the softmax function, and the animal condition information issorted by codes indicating abnormal conditions of animals includingstop, walk, run, limp, delivery, excretion, heat, fell over, stuck,biting and nose rubbing.
 13. The method for identifying a condition ofan animal object based on an image of claim 9, wherein in the process ofextracting continuous animal detection information, information aboutthe class of the animal object detected from the image and informationabout a pose of the animal object are further extracted as the objectdetection data.
 14. The method for identifying a condition of an animalobject based on an image of claim 9, wherein the animal detection modelis constructed based on the n number of continuous entire imagesincluding at least one animal object and learning data in which theanimal detection information is matched with the animal object includedin each of the continuous entire images, and the animal detection modelincludes a backbone configured to extract a feature from the inputimage, a neck configured to collect intermediate information from eachlayer of the backbone based on the feature extracted by the backbone,and a head configured to output the animal detection information basedon the intermediate information collected by the neck.
 15. The methodfor identifying a condition of an animal object based on an image ofclaim 14, wherein the head of the animal detection model extracts abounding box of the animal object and a keypoint of the animal objectbased on cascaded multi-lane deep convolutional networks, and thecascaded multi-lane deep convolutional networks are constructed toperform a process of extracting coordinates of a major keypoint, aprocess of extracting a direction of a tangent line passing through thecoordinates of the major keypoint and a process of extracting a widthand a height of an area including the tangent line and the majorkeypoint.
 16. The method for identifying a condition of an animal objectbased on an image of claim 9, wherein the head of the animal detectionmodel is constructed to extract each of information about the class ofthe animal object and information about a pose of the animal objectbased on a single-lane deep convolutional network.
 17. A non-transitorycomputer-readable recording medium that stores therein a computerprogram configured to perform an image-based animal object conditionidentification method of claim 9.